Potential of Genomic Breeding Program in Iranian Native Chickens

محورهای موضوعی : Camel

س. ابراهیم‏پورطاهر ¹ (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
ص. علیجانی ² (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
س.ع. رأفت ³ (Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran)
ا.ر. شریفی ⁴ (Department of Animal Science, Faculty of Agriculture, University of Göttingen, Göttingen, Germany)

تاریخ دریافت : 1396/07/05 تاریخ پذیرش : 1396/11/26 تاریخ انتشار : 1397/06/10

کلید واژه: simulation, genetic gain, genomic selection, economic profit,

چکیده مقاله :

Development of genomic selection can be a new strategy in breeding of native chicken. The main aim of this study was to evaluate application of a genomic selection program in Iranian native chickens from economic and genetic points of view. In this study, two scenarios including conventional scenario with 3360 and 3380 animals and genomic scenario were compared using ZPLAN+ software. The traits in the selection index were egg number, body weight, mean weight of egg and age at sexual maturity. In genomic scenario different reference population size were considered. In this scenario the genomic information from cocks (800 cocks were genotyped) was added to available information in conventional scenario based on selection index method. The generation interval was 14.5 months for all conventional and genomic scenarios. In comparison of scenarios, genetic gain and the economic profit increased by increasing reference population size in genomic scenario (€126.88-€147.45 with 80 cocks) and (€140.20 to €160.77 with 60 cocks) per animal unit. The reliability of selection index was 0.33 for cocks in conventional scenario. The reliabilities of genomic scenarios were 0.61-0.84 for 80 selected cocks and 0.66-0.87 for 60 selected cocks and that were high in comparison to conventional scenario. This study showed that genomic selection can increase the genetic improvement rate of native chickens. However, the costs of genomic scenarios were higher than conventional scenario, but genomic information increased accuracy of selection and genetic gain in breeding goals traits.

چکیده انگلیسی:

گله­های بومی در کشورهای کم درآمد و در حال توسعه همچون ایران، نقش مهمی ایفا می‏کنند. توسعه انتخاب ژنومی می‏تواند یک استراتژی جدید در اصلاح نژاد مرغان بومی باشد. هدف از این مطالعه بررسی کاربرد برنامه انتخاب ژنومی در مرغان بومی ایران از لحاظ اقتصادی و از دیدگاه ژنتیکی بود. در این مطالعه دو سناریو شامل سناریوی مرسوم با 3360 و 3380 حیوان با یک سناریوی ژنومی با استفاده از نرم‏افزار ZPLAN+ مقایسه شد. صفات موجود در شاخص انتخاب، تعداد تخم‏ مرغ، وزن بدن، متوسط وزن تخم ‏مرغ و سن بلوغ جنسی بود. در سناریوی ژنومی اندازه جمعیت مرجع متفاوت مد نظر بودند. در این سناریو اطلاعات ژنومی خروس‏ها (800 خروس تعیین ژنوتیپ شده بود) به اطلاعات موجود در سناریوی مرسوم بر پایه متد شاخص انتخاب اضافه شدند. فاصله بین‏نسلی در همه سناریوهای مرسوم و ژنومی 5/14 ماه بود. در مقایسه سناریوها، رشد ژنتیکی و سود اقتصادی در سناریوی ژنومی با افزایش اندازه جمعیت مرجع افزایش یافت (88/126 یورو تا 45/147 یورو با 80 خروس) و (20/140 یورو تا 77/160 یورو با 60 خروس) در هر واحد حیوانی. قابلیت اعتماد شاخص انتخاب در سناریوی مرسوم در مسیر خروس‏ها 33/0 بود. قابلیت اعتماد در سناریو ژنومی با 80 خروس انتخابی برابر 61/0 تا 84/0 و در سناریوی ژنومی با 60 خروس انتخابی 66/0 تا 87/0 بود و در مقایسه با سناریوی مرسوم بالاتر بود. این مطالعه نشان داد که انتخاب ژنومی می‏تواند نرخ پیشرفت ژنتیکی را در مرغان بومی افزایش دهد. در حالیکه هزینه‏های سناریوهای ژنومی از سناریوی مرسوم بالاتر بود اما اطلاعات ژنومی دقت و رشد ژنتیکی را در صفات هدف اصلاحی افزایش داد.

منابع و مأخذ:

Alshami A. (2014). Optimization of selection for multiple traits using an economic model for layer chicken enterprise. Int. J. Sci. Technol. Res. 3, 89-90.

Atiyat M. (2014). Sustainable breeding program of Black Bedouin goat for conserving genetic diversity: simulated scenarios for in situ conservation. Jordan J. Agric. Sci. 10, 83-95.

Deimi D. and Alijani S. (2011). Genetic dissection of some production traits of Fars province native chickens using statistical methods. J. Anim. Sci. Res. 21, 71-81.

Dekkers J.C.M. (2007). Prediction of response to marker- assisted and genomic selection using selection index theory. J. Anim. Breed. Genet. 124, 331-341. 

Erbe M., Renhardt F. and Simianer H. (2011). Empirical determination of the number of independent chromosome segments based on cross-validated data. Pp. 115 in Proc. 62^nd in Book of Abstr. European Feder. Anim. Sci. Conf. Stavanger, Norway.

Goddard M.E. and Hayes B.J. (2007). Genomic selection. J. Anim. Breed. Genet. 124, 323-330.

Goddard M. (2009). Genomic selection: prediction of accuracy and maximisation of long term response. Genetica. 136, 245-257.

Groenen M.A.M., Wahlberg P., Foglio M., Cheng H.H., Megens H.J., Crooijmans R., Besnier F., Lathrop M., Muir W.M., Wong G.K., Gut I. and Andersson L. (2009). A high density SNP based linkage map of the chicken genome reveals sequence features correlated with recombination rate. Genome Res. 19, 510-519.

Haberland A.M., Luther H., Hofer A., Tholen E., Simianer H., Lind B. and Baes C. (2013). Efficiency of different selection strategies against boar taint in pigs. Animal. 8, 11-19.

Haberland A.M., Ytournel F., Luther H. and Simianer H. (2010). Evaluation of selection strategies including genomic breeding values in pigs. Pp. 1-26 in Proc. 61^st Ann. Meet. Eur. Feder. Anim. Sci. Heraklion, Crete, Greece.

Hazel L.N. (1943). The genetic basis for constructing selection indexes. Genetics. 28, 476-490.

Hill W.G. (1974). Prediction and evaluation of response to selection with overlapping generations. Anim. Prod. 18, 117-139.

Hillier L.W., Miller W., Birney E., Warren W., Hardison R.C., Ponting C.P. and Bork P. (2004). Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 432, 695-716.

Kiani manesh H., Nejati Javaremi A. and Kamali M. (2001). Estimation of genetic and environment parameters economic important traits for Fars native fowls. Anim. Sci. J. (Pajouhesh and Sazandegi). 4, 6-9.

König S., Simianer H. and Willam A. (2009). Economic evaluation of genomic breeding programs. J. Dairy Sci. 92, 382-391.

Meuwissen T.H.E., Hayes B.J. and Goddard M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829.

Nejati Javaremi A., Smith C. and Gibson J.P. (1997). Effect of total allelic relationship on accuracy of evaluation and response to selection. J. Anim. Sci. 75, 1738-1745.

Niknafs S.H., Abdi H., Fatemi S.A., Zandi M.B. and Baneh H. (2013). Genetic trend and inbreeding coefficients effects for growth and reproductive traits in Mazandaran indigenous chicken. J. Biol. 3, 25-31.

Nitter G. and Graser H.U. (1994). ZPLAN – a PC program to optimize livestock selection programs. Pp. 77-78 in Proc. 5^th World Congr. Genet. Appl. Livest. Prod. Guelph, Canada.

Sitzenstock F., König S., Ytournel F. and Simianer H. (2010). Evaluation of genomic selection for functional traits in horse breeding programs. Pp. 55-58 in Proc. 9^th World Congr. Genet. Appl. Livest. Prod. Univ. Gottingen, Germany.

Sitzenstock F., Ytournel F., Sharifi A.R., Cavero D., Täubert H., Preisinger R. and Simianer H. (2013). Efficiency of genomic selection in an established commercial layer breeding program. Genet. Sel. Evol. 45, 1-11.

Täubert H., Reinhardt F. and Simianer H. (2010). ZPLAN+ new software to evaluate and optimize animal breeding programs. Pp. 71-73 in Proc. 9^th World Congr. Genet. Appl. Livest. Prod. Univ. Gottingen, Germany.

Van der Werf J. (2013). Genome-Wide Association Studies and Genomic Prediction. Humana Press, New York, United States.

Wolc A. (2015). Genomic selection in layer and broiler breeding. Lohmann Inform. 49(1), 4-11.

Yousefi Zonuzi A., Alijani S., Rafat S.A., Abbasi M.A. and Daghigh Kia A. (2013). Estimation of maternal effects on the north-Iranian native chicken traits using Bayesian and REML methods. Slovak J. Anim. Sci. 46(2), 52-60.